The reporting of statistical significance in scientific journals

Max-Planck-Institut f?r demografische Forschung Max Planck Institute for Demographic Research Konrad-Zuse-Strasse 1 ? D-18057 Rostock ? GERMANY

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MPIDR WORKING PAPER WP 2007-037 DECEMBER 2007

The reporting of statistical significance in scientific journals

Jan M. Hoem (hoem@demogr.mpg.de)

? Copyright is held by the authors. Working papers of the Max Planck Institute for Demographic Research receive only limited review. Views or opinions expressed in working papers are attributable to the authors and do not necessarily reflect those of the Institute.

JMH/-, 5 April 2001

The reporting of statistical significance in scientific journals

A reflexion by Jan M. Hoem

Scientific journals in most empirical disciplines have regulations about how authors should report the precision of their estimates of model parameters and other model elements. Some journals that overlap fully or partly with the field of demography demand as a strict prerequisite for publication that a p-value, a confidence interval, or a standard deviation accompany any parameter estimate.1 I feel that this rule is sometimes applied in an overly mechanical manner. Standard deviations and p-values produced routinely by general-purpose software are taken at face value and included without questioning, and features that have too high a p-value or too large a standard deviation are too easily disregarded as being without interest because they appear not to be statistically significant. In my opinion authors should be discouraged from adhering to this practice, and flexibility rather than rigidity should be encouraged in the reporting of statistical significance. One should also encourage thoughtful rather than mechanical use of p-values, standard deviations, confidence intervals, and the like. Here is why:

1. The scientific importance of an empirical finding depends much more on its contribution to the development or falsification of a substantive theory than on the values of indicators of statistical significance. It is important that authors be guided by a process of discovery and not blinded by a lack of statistical significance in the description of an empirical pattern. This means that authors should feel free to report findings that appear not to be statistically significant, provided that this fact is also reported. Indicators of statistical significance should not be suppressed, but authors should avoid using them mechanically.

2. Measures of statistical significance may be misleading. When a model has been developed through repeated use of tests of significance to include and exclude covariates, to split or combine levels on categorical covariates, and to determine other model features, the user often loses control over statistical-significance values, and the values computed by standard software may be completely misleading. If one mechanically includes the p-values cranked out by standard software, this serves sooner to mislead than to inform.

3. Standard p-values can be insufficiently precise indicators of statistical significance, particularly if their values are given only in grouped levels, which are often indicated by asterisks beside parameter estimates ("* = p ................
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